{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import xgboost as xgb\n",
    "import sys,random\n",
    "import pickle\n",
    "import os\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1.load data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 6.96 s, sys: 532 ms, total: 7.5 s\n",
      "Wall time: 7.5 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "input_path = '../../preprocess_data/'\n",
    "train_x_int = pd.read_csv(input_path + 'train_x_int.csv').drop(columns=['tag'])\n",
    "unlabel_x_int =  pd.read_csv(input_path + 'unlabel_x_int.csv').drop(columns=['tag'])\n",
    "\n",
    "train_int = pd.concat([train_x_int, unlabel_x_int],axis=0)\n",
    "valid_int = pd.read_csv(input_path + 'valid_int.csv')\n",
    "train_y = pd.read_csv(input_path + 'train_y_33465.csv')\n",
    "train_y = train_y.label\n",
    "\n",
    "train_id = train_x_int.id\n",
    "unlabel_id = unlabel_x_int.id\n",
    "valid_id = valid_int.id\n",
    "\n",
    "train_x_int.drop(columns=['id'], inplace=True)\n",
    "unlabel_x_int.drop(columns=['id'], inplace=True)\n",
    "valid_int.drop(columns=['id'], inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2.根据unique num和阈值，选出代表类别类型的数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "int_uniq = [[col,train_int[col].unique().shape[0]] for col in train_int.columns]\n",
    "int_uniq_sort = sorted(int_uniq,key=lambda x: x[1],reverse=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7fbfd6c0aac8>]"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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qv5z2z6iqT1fVtqr646o6Zip/2rS/bTp++sxnvHMq/1JVvXamfMtUtq2q3nGwdQUAOBDrsUv07Ulundn/nSTv6e4XJrk/yYVT+YVJ7p/K3zOdl6o6K8mbk/xAki1Jfn8KgRuSvD/J65KcleQt07kAAGtmwB7RgwtsVXVqkp9M8sFpv5K8KslHp1MuS/KGafu8aT/T8XOn889LckV3P9LdX06yLcnZ09e27r69ux9NcsV0LgDAmumsvzcd/G6SX0uye9p/TpJvdPfOaX97klOm7VOS3Jkk0/EHpvOXypdds7/yfVTVRVW1taq27tix4yB/JADgSLau3iVaVT+V5N7u/sxTWJ9V6e5Luntzd2/etGnTvKsDABzuBktsGw/i2lck+emqen2SY5M8K8l7k5xQVRunVrRTk9w1nX9XktOSbK+qjUmeneTrM+WLZq/ZXzkAwJoYcAjb6lvYuvud3X1qd5+ehUkDn+jun0vyySRvnE67IMnHpu2rpv1Mxz/RCwudXJXkzdMs0jOSnJnk75PckOTMadbpMdM9rlptfQEADkiP96aDg2lh25//nOSKqvqtJDcm+dBU/qEkH6mqbUnuy0IAS3ffXFVXJrklyc4kb+vuXUlSVb+U5JokG5Jc2t03r0F9AQCWLEw6mHct9vaUBLbu/pskfzNt356FGZ7Lz/lOkp/Zz/XvTvLuFcqvTnL1U1FHAIADNVhe86YDAIBZ624dNgCA9aazPt90AACwrow26UBgAwCY0QP2iQpsAAAzdIkCAAxuXb2aCgBg3RqsiU1gAwAYnMAGADBZnHAwVvuawAYAsGRxguhgPaICGwDA6AQ2AIDJ4gpsFs4FABjU0hi2sfKawAYAsNxgeU1gAwBYNN5LqRYIbAAAE7NEAQAG11kcwzZWYhPYAAAGJ7ABAEx60EFsAhsAwDKD9YgKbAAAy1k4FwBgULpEAQAGt2eW6JwrsozABgAwWVqHbb7V2IfABgCwjBY2AIBBDTqETWADAFjUU5+oWaIAAINabGHTJQoAwJMisAEATKzDBgAwusVlPQbrExXYAACWGSuuCWwAAEt60IU9BDYAgMnSmw4Ga2IT2AAAJkvLesy1FvsS2AAAljHpAABgUD3ouh4CGwDAxJsOAAAGtzTpYL7V2IfABgAwOIENAGDSGXNdD4ENAGCRLlEAgMPDYA1sAhsAwKIxF/UQ2AAAluyZJTpWE5vABgAwWZx0oEsUAGBwg+U1gQ0AYNGgb6YS2AAAFnk1FQDAYcKkAwCAQfWgfaICGwDAZCmvjdXAJrABACw3WF4T2AAAlqvBZh0IbAAAk0GHsAlsAACLlt50MOd6LCewAQBMlt4lOlhiE9gAAJYR2AAABjXoEDaBDQBg0eLCud50AAAwOF2iAACD0iUKADA467ABAAxvGsM2WJ+owAYAsMxYcU1gAwBYoksUAGBwi3ltsB5RgQ0AYNHSq6kG6xQV2AAABiewAQBMemmW6JwrssyqA1tVnVZVn6yqW6rq5qp6+1R+UlVdW1W3Td9PnMqrqt5XVduq6vNV9bKZz7pgOv+2qrpgpvyHq+qm6Zr31WhzbAGAdWVPl+hYDqaFbWeSX+3us5Kck+RtVXVWknckua67z0xy3bSfJK9Lcub0dVGSDyQLAS/JxUlenuTsJBcvhrzpnF+YuW7LQdQXAOCAjNZEtOrA1t13d/dnp+1vJrk1ySlJzkty2XTaZUneMG2fl+TyXnB9khOq6vlJXpvk2u6+r7vvT3Jtki3TsWd19/W98CbWy2c+CwDgKbeul/WoqtOTvDTJp5Oc3N13T4e+muTkafuUJHfOXLZ9Knu88u0rlK90/4uqamtVbd2xY8dB/SwAwJGr9yzsMdd6LHfQga2qnpHkT5L8Snc/OHtsahlb86za3Zd09+bu3rxp06a1vh0AsE4tjWEbK68dXGCrqqOzENb+sLv/dCq+Z+rOzPT93qn8riSnzVx+6lT2eOWnrlAOALCmBstrBzVLtJJ8KMmt3f0/Zg5dlWRxpucFST42U37+NFv0nCQPTF2n1yR5TVWdOE02eE2Sa6ZjD1bVOdO9zp/5LACAI8bGg7j2FUn+XZKbqupzU9mvJ/ntJFdW1YVJ7kjypunY1Ulen2RbkoeTvDVJuvu+qvrNJDdM572ru++btn8xyYeTHJfk49MXAMCa2NMlOlYb26oDW3d/KvtvMTx3hfM7ydv281mXJrl0hfKtSV682joCAKzGWHHNmw4AAJb02s+VXBWBDQBgsi5niQIArCdLq7AJbAAAY6vBRrEJbAAAkx703VQCGwDAZCmujdXAJrABACxamnQw32rsQ2ADAFhmtIVzBTYAgCXGsAEADE2XKADAYWKwHlGBDQBg0ZgdogIbAMCSPV2iYzWxCWwAAJPFhXN1iQIADG6wvCawAQAsMoYNAGBwS68SHayJTWADAJj01MZm0gEAAE+KwAYAsGhxWY+xGtgENgCARYMOYRPYAACWq8Ga2AQ2AIBJD7quh8AGADBZmiU6VgObwAYAsGjPu0THIrABACyjhQ0AYFCDDmET2AAAFvWg76YS2AAAltElCgAwKF2iAACjM0sUAGBse9ZhGyuyCWwAAMuMFdcENgCAJV5NBQAwuKU3HQzWxCawAQBM9qzCNlZiE9gAAJbRwgYAMKgedBCbwAYAMBkzrglsAAD70CUKADCoQXtEBTYAgD2mNx2YJQoAMCbrsAEADG77/d9OkjzvmU+bc032JrABAExuufvBfNezjs1zniGwAQAM6ZavPJizvvtZ867GPgQ2AIAk33lsV7bteChnPV9gAwAY0rZ7H8qu3a2FDQBgVFduvTNJ8oOnPHvONdmXwAYAHPFu/Kf7c/nf3ZG3vuL0nHbS8fOuzj4ENgDgiPep276WJPmPr/6+OddkZQIbAHDEu+3eh3LqicflmccePe+qrEhgAwCOeLfd+1Be+LxnzLsa+yWwAQBHtF27O7fveChnCmwAAGPafv/DeWTnbi1sAACj2nbvQ0mSFz7vmXOuyf4JbADAEe3mrzyYquTMk7WwAQAMaesd9+dFJz8zzxp0hmgisAEAR7BduzufveP+bD79xHlX5XEJbADAEeuLX30wDz2yMz9y+knzrsrjEtgAgCPSPQ9+J7/xF7ckSTYLbAAA4/mfn7gtn7vzG/mv//oHc8oJx827Oo9LYAMAjji7d3euufmevPqfnZyfffkL5l2dJySwAQBHnBvvvD87vvlIXvvi75p3VQ6IwAYAHFG++Z3H8lt/dWuOPfqo/MSLNs27OgdEYAMAjhi7d3d+5YrP5abtD+R3/81L88yB116btXHeFQAAWGt/8Q9fyZVb78y9Dz6SL93zzfzGT/9Athwm3aGJwAYArHN/9fm78/YrbswLTjo+Zzz36flXL3l+zv/R75l3tZ4UgQ0AWJce2bkrH/m7O/LfPv7FvPQFJ+YjF56d4485PKPP4VlrAID9uGn7A/nc9m/k0k99OV/+2rfyEy/alN/72ZcdtmEtOQwCW1VtSfLeJBuSfLC7f3vOVQIABvTQIzvzX/78C/mzG+9Kkpx20nH5g7f+SF75fZtSVXOu3cEZOrBV1YYk70/y6iTbk9xQVVd19y3zrRkAsBrdnZ27Ozt3dR7bvTs7d3V27tqdR3dN27t357Fdncd27c63H92Vhx/blW8/uivfemRnvv3Yrjz86MLXtx/dOX1f2P/Woztz010P5MFvP5ZfftUL86YfOS3f9axjs3HD+lgQY+jAluTsJNu6+/YkqaorkpyXZG6B7X9df0euufmrh+x+3au8Lqu7cNX3O1zqubrLVn3h4fLz9SpvuPr7rfK6Vd5vtTc89D/f4fLf7Wrvt+onuMr7rfK6p+g5LP+U5T//Pnc5yOv3vX/vc3zxnMXP6sXy6dyF7cXzlpft+cSFz5r2Fz9zpXPSS9cnC0tq7Nz91Pw9OPboo3L8MRtz3NEb8vSnbchxx2zMq77/efnZs18w/HtBV2P0wHZKkjtn9rcnefnyk6rqoiQXJckLXrC2r5f4zmO78tAjO1d17WobY1fbjLv6+63yutXe8RBeVlnlz1er/flq9X+eh/g5HOregsPn7/Vq73d4/HyrveOh//Nc7f0O8c+37Lp97v/4u/v8vdn3+MFdv/i7rGrPsZop23OP/Z+zp6z2qtPs8T2fNf0JTNcfVcnGDUfl6KNq4fuGysZp+5gNR2Xjhtrr+PHHbMhxx2zI8cdsyPFHb1zaPu7oDTnqqMO7i/PJGj2wHZDuviTJJUmyefPmNf0n3M//+Pfm53/8e9fyFgAAexm9Y/euJKfN7J86lQEAHDFGD2w3JDmzqs6oqmOSvDnJVXOuEwDAITV0l2h376yqX0pyTRaW9bi0u2+ec7UAAA6poQNbknT31Umunnc9AADmZfQuUQCAI57ABgAwOIENAGBwAhsAwOAENgCAwQlsAACDE9gAAAYnsAEADE5gAwAYnMAGADA4gQ0AYHACGwDA4AQ2AIDBCWwAAIOr7p53HZ5SVbUjyR1rfJvnJvnaGt+D1fFsxuXZjM3zGZdnM66n4tl8T3dveqKT1l1gOxSqamt3b553PdiXZzMuz2Zsns+4PJtxHcpno0sUAGBwAhsAwOAEttW5ZN4VYL88m3F5NmPzfMbl2YzrkD0bY9gAAAanhQ0AYHACGwDA4AS2J6mqtlTVl6pqW1W9Y971OdJU1aVVdW9VfWGm7KSquraqbpu+nziVV1W9b3pWn6+ql82v5utfVZ1WVZ+sqluq6uaqevtU7vnMWVUdW1V/X1X/MD2b35jKz6iqT0/P4I+r6pip/GnT/rbp+OnzrP+RoKo2VNWNVfWX075nM4Cq+sequqmqPldVW6eyufxOE9iehKrakOT9SV6X5Kwkb6mqs+ZbqyPOh5NsWVb2jiTXdfeZSa6b9pOF53Tm9HVRkg8cojoeqXYm+dXuPivJOUneNv334fnM3yNJXtXdL0nyQ0m2VNU5SX4nyXu6+4VJ7k9y4XT+hUnun8rfM53H2np7kltn9j2bcfxEd//QzHprc/mdJrA9OWcn2dbdt3f3o0muSHLenOt0ROnuv01y37Li85JcNm1fluQNM+WX94Lrk5xQVc8/NDU98nT33d392Wn7m1n4n88p8XzmbvozfmjaPXr66iSvSvLRqXz5s1l8Zh9Ncm5V1SGq7hGnqk5N8pNJPjjtVzybkc3ld5rA9uSckuTOmf3tUxnzdXJ33z1tfzXJydO25zUnUzfNS5N8Op7PEKYut88luTfJtUn+X5JvdPfO6ZTZP/+lZzMdfyDJcw5tjY8ov5vk15LsnvafE89mFJ3kr6vqM1V10VQ2l99pG5+qD4IRdHdXlbVq5qiqnpHkT5L8Snc/OPuPf89nfrp7V5IfqqoTkvxZku+fc5VIUlU/leTe7v5MVb1y3vVhHz/W3XdV1fOSXFtVX5w9eCh/p2lhe3LuSnLazP6pUxnzdc9is/P0/d6p3PM6xKrq6CyEtT/s7j+dij2fgXT3N5J8MsmPZqHLZvEf7rN//kvPZjr+7CRfP8RVPVK8IslPV9U/ZmGYzauSvDeezRC6+67p+71Z+IfO2ZnT7zSB7cm5IcmZ0+ydY5K8OclVc64TC8/ggmn7giQfmyk/f5q5c06SB2aasXmKTeNoPpTk1u7+HzOHPJ85q6pNU8taquq4JK/OwhjDTyZ543Ta8mez+MzemOQTbZX1NdHd7+zuU7v79Cz8P+UT3f1z8WzmrqqeXlXPXNxO8pokX8icfqd508GTVFWvz8J4gw1JLu3ud8+5SkeUqvqjJK9M8twk9yS5OMmfJ7kyyQuS3JHkTd193xQgfi8Ls0ofTvLW7t46j3ofCarqx5L83yQ3Zc9YnF/Pwjg2z2eOquqfZ2Fw9IYs/EP9yu5+V1V9bxZadU5KcmOSf9vdj1TVsUk+koVxiPcleXN33z6f2h85pi7R/9TdP+XZzN/0DP5s2t2Y5H9397ur6jmZw+80gQ0AYHC6RAEABiewAQAMTmADABicwAYAMDiBDQBgcAIbAMDgBDYAgMH9f7qX/xWyGN1qAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,8))\n",
    "plt.plot((np.array(int_uniq_sort)[:,1]).astype(int))\n",
    "\n",
    "plt.figure(figsize=(10,8))\n",
    "plt.plot(np.log((np.array(int_uniq_sort)[:,1]).astype(int)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 由上图可知，纵坐标取对数后，阶梯递增的曲线持续到3左右，也就是1000\n",
    "cat_cols = [col for col,num in int_uniq_sort if num>1 and num<1000]\n",
    "\n",
    "train_x_int_cat = train_x_int[cat_cols]\n",
    "unlabel_x_int_cat = unlabel_x_int[cat_cols]\n",
    "valid_int_cat = valid_int[cat_cols]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2.计算特征与label之间的互信息\n",
    "注意：只使用了有标签的数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "from sklearn.feature_selection import mutual_info_classif\n",
    "mutual_info = mutual_info_classif(train_x_int_cat.values, train_y.values)\n",
    "\n",
    "thre = 0.0015\n",
    "selec_cols = [train_x_int_cat.columns[i] for i in range(len(mutual_info)) if mutual_info[i]>thre] # 取相关度大于0.002的离散特征\n",
    "\n",
    "train_x_int_filter = train_x_int_cat[selec_cols]\n",
    "unlabel_x_int_filter = unlabel_x_int_cat[selec_cols]\n",
    "valid_int_filter = valid_int_cat[selec_cols]\n",
    "\n",
    "train_x_int_filter['id'] = train_id\n",
    "unlabel_x_int_filter['id'] = unlabel_id\n",
    "valid_int_filter['id'] = valid_id\n",
    "\n",
    "train_x_int_filter.to_csv('../../preprocess_data/train_x_int_filter.csv',index=False)\n",
    "unlabel_x_int_filter.to_csv('../../preprocess_data/unlabel_x_int_filter.csv',index=False)\n",
    "valid_int_filter.to_csv('../../preprocess_data/valid_int_filter.csv',index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "正负样本不均衡 {0.0: 31267, 1.0: 2198}\n"
     ]
    }
   ],
   "source": [
    "# 生成dmatrix\n",
    "# train_x = train_x_int_filter.drop(columns=['id'])\n",
    "train_x = train_x_int_cat\n",
    "\n",
    "print('正负样本不均衡',train_y.value_counts().to_dict())\n",
    "dtrain = xgb.DMatrix(train_x, feature_names=train_x.columns, label=train_y)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 训练xgb model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def pipeline(dtrain,dtest,test_id,iteration,random_seed,gamma,max_depth,lambd,subsample,colsample_bytree,min_child_weight):\n",
    "    if max_depth==6:\n",
    "        num_boost_round = 1500\n",
    "    elif max_depth==7:\n",
    "        num_boost_round = 1400\n",
    "    elif max_depth==8:\n",
    "        num_boost_round = 1200\n",
    "    \n",
    "    params={\n",
    "    'booster':'gbtree',\n",
    "    'objective': 'binary:logistic',\n",
    "    'early_stopping_rounds':50,\n",
    "    'scale_pos_weight': float(len(train_y)-np.sum(train_y.values))/float(np.sum(train_y.values)),  # 负例样本除以正例样本\n",
    "    'eval_metric': 'auc',\n",
    "    'gamma':gamma,\n",
    "    'max_depth':max_depth,\n",
    "    'lambda':lambd,\n",
    "    'subsample':subsample,\n",
    "    'colsample_bytree':colsample_bytree,\n",
    "    'min_child_weight':min_child_weight, \n",
    "    'eta': 0.04,\n",
    "    'seed':random_seed,\n",
    "    'nthread':16\n",
    "        }\n",
    "    watchlist  = [(dtrain,'train')]\n",
    "    model = xgb.train(params,dtrain,num_boost_round=num_boost_round,evals=watchlist)\n",
    "    model.save_model('./model/xgb{0}.model'.format(iteration))\n",
    "    \n",
    "    #predict test set\n",
    "    test_y = model.predict(dtest)\n",
    "    test_result = pd.DataFrame(test_id,columns=[\"id\"])\n",
    "    test_result['score'] = test_y\n",
    "    test_result.to_csv(\"./preds/xgb{0}.csv\".format(iteration),index=None,encoding='utf-8')\n",
    "    \n",
    "    #get feature score\n",
    "    feature_score = model.get_fscore()\n",
    "    feature_score = sorted(feature_score.items(), key=lambda x:x[1],reverse=True)\n",
    "    fs = []\n",
    "    for (key,value) in feature_score:\n",
    "        fs.append(\"{0},{1}\\n\".format(key,value))\n",
    "    \n",
    "    with open('./featurescore/feature_score_{0}.csv'.format(iteration),'w') as f:\n",
    "        f.writelines(\"feature,score\\n\")\n",
    "        f.writelines(fs)\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "[1494]\ttrain-auc:0.848761\n",
      "[1495]\ttrain-auc:0.848856\n",
      "[1496]\ttrain-auc:0.848983\n",
      "[1497]\ttrain-auc:0.848985\n",
      "[1498]\ttrain-auc:0.849005\n",
      "[1499]\ttrain-auc:0.849048\n",
      "CPU times: user 46min 18s, sys: 12.7 s, total: 46min 31s\n",
      "Wall time: 3min 14s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "random_seed = list(range(0,100000,1000))\n",
    "gamma = [i/1000.0 for i in range(100,200,2)]\n",
    "max_depth = [6,7,8]\n",
    "lambd = list(range(200,400,2))\n",
    "subsample = [i/1000.0 for i in range(600,700,2)]\n",
    "colsample_bytree = [i/1000.0 for i in range(250,350,2)]\n",
    "min_child_weight = [i/1000.0 for i in range(200,300,2)]\n",
    "random.shuffle(random_seed)\n",
    "random.shuffle(gamma)\n",
    "random.shuffle(max_depth)\n",
    "random.shuffle(lambd)\n",
    "random.shuffle(subsample)\n",
    "random.shuffle(colsample_bytree)\n",
    "random.shuffle(min_child_weight)\n",
    "\n",
    "with open('params.pkl','wb') as f:\n",
    "    pickle.dump((random_seed,gamma,max_depth,lambd,subsample,colsample_bytree,min_child_weight),f)\n",
    "\n",
    "#to reproduce my result, uncomment following lines\n",
    "\"\"\"\n",
    "with open('params_for_reproducing.pkl','rb') as f:\n",
    "    random_seed,gamma,max_depth,lambd,subsample,colsample_bytree,min_child_weight = pickle.load(f)    \n",
    "\"\"\"\n",
    "\n",
    "for i in range(8):\n",
    "    pipeline(dtrain,dvalid,valid_id,i,random_seed[i],gamma[i],max_depth[i%3],lambd[i],subsample[i],colsample_bytree[i],min_child_weight[i])"
   ]
  }
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